########################################################################
##
##  Purpose: This script produces Figure 2 from the manuscript
##  Author: james.h.bisbee@vanderbilt.edu
##  Date: 05/18/2024
##
##  Input Files:
##    - ./data/for_replication.rds
##  Output Files:
##    - ./output/Figures/MUSK_fig2.pdf
##
########################################################################

rm(list = ls())
gc()

require(tidyverse)
require(ggridges)
require(changepoint)

# Compute details
Sys.info()
# sysname        release        version       nodename 
# "Windows"       "10 x64"  "build 22631"   "AS-GR3RGY3" 
# machine          login           user effective_user 
# "x86-64"     "bisbeejh"     "bisbeejh"     "bisbeejh"

ram_size = system("wmic MemoryChip get Capacity", intern = TRUE)[-1]
model_name = system("wmic cpu get name", intern = TRUE)[2] # nocov
vendor_id = system("wmic cpu get manufacturer", intern = TRUE)[2] # nocov

print(list(ram = stringr::str_squish(ram_size)[1],
           vendor_id = stringr::str_squish(vendor_id),
           model_name = stringr::str_squish(model_name),
           no_of_cores = parallel::detectCores()))

# $ram
# [1] "17179869184"
# 
# $vendor_id
# [1] "GenuineIntel"
# 
# $model_name
# [1] "13th Gen Intel(R) Core(TM) i9-13900H"
# 
# $no_of_cores
# [1] 20

# Package details
sessionInfo()

# attached base packages:
# [1] stats     graphics  grDevices utils     datasets 
# [6] methods   base     
# 
# other attached packages:
#  [1] changepoint_2.2.4 zoo_1.8-12        ggridges_0.5.4   
#  [4] lubridate_1.9.2   forcats_1.0.0     stringr_1.5.0    
#  [7] dplyr_1.1.2       purrr_1.0.1       readr_2.1.4      
# [10] tidyr_1.3.0       tibble_3.2.1      ggplot2_3.4.4    
# [13] tidyverse_2.0.0  
# 
# loaded via a namespace (and not attached):
#  [1] vctrs_0.6.3       cli_3.6.1         rlang_1.1.1      
#  [4] stringi_1.7.12    generics_0.1.3    glue_1.6.2       
#  [7] colorspace_2.1-0  hms_1.1.3         scales_1.3.0     
# [10] fansi_1.0.4       grid_4.3.3        munsell_0.5.0    
# [13] tzdb_0.4.0        lifecycle_1.0.3   compiler_4.3.3   
# [16] timechange_0.2.0  pkgconfig_2.0.3   rstudioapi_0.15.0
# [19] lattice_0.22-5    R6_2.5.1          tidyselect_1.2.0 
# [22] utf8_1.2.3        pillar_1.9.0      magrittr_2.0.3   
# [25] tools_4.3.3       withr_2.5.0       gtable_0.3.3 

finalFullUsers <- read_rds('./data/for_replication.rds')

toMerge <- finalFullUsers %>%
  filter(type == 'Retweet') %>%
  mutate(created_at = lubridate::round_date(created_at,unit = 'week',week_start = 3)) %>%
  group_by(sn,created_at) %>%
  summarise(nTweets = sum(nTweets,na.rm=T),
            nAccounts = length(unique(sn))) %>%
  ungroup()


# Weekly change
grid <- expand.grid(created_at = lubridate::as_datetime(seq.Date(from = as.Date('2020-01-01'),to = as.Date('2023-02-01'),by = 'week')),
                    sn = unique(toMerge$sn))

toplot <- grid %>% 
  left_join(toMerge) %>%
  mutate_at(vars(matches('nTweet|nAccount')),function(x) ifelse(is.na(x),0,x)) %>%
  as_tibble() %>%
  group_by(sn) %>%
  arrange(created_at) %>%
  mutate(diffTweets = nTweets - lag(nTweets,n = 52),
         diffAccounts = nAccounts - lag(nAccounts,n = 52)) %>%
  group_by(created_at) %>%
  mutate(nUsers = n(),
         nTweets = sum(nTweets)) %>%
  summarise_at(vars(matches('^n|diff')),mean)

pdf('./output/Figures/MUSK_fig2.pdf',width = 7,height = 5)
toplot %>%
  filter(created_at > as.Date('2022-01-01')) %>%
  ggplot(aes(x = created_at,y = diffTweets,fill = diffTweets < 0,group = 1)) + 
  geom_bar(stat = 'identity') + 
  geom_smooth(method = 'gam') + 
  scale_fill_manual(guide = 'none',values = c('darkgreen','darkred')) + 
  theme_bw() + 
  geom_vline(xintercept = lubridate::as_datetime('2022-10-28'),
             size = 1.1,linetype = 'dashed',color = 'red') + 
  annotate(geom = 'label',x = lubridate::as_datetime('2022-10-28'),
           y = Inf,label = 'Musk',
           angle = 90,vjust = .5,hjust = 1) + 
  labs(title = 'Annual Change in Weekly Tweets',
       x = 'Date',
       y = 'Annual Change') + 
  xlim(lubridate::as_datetime(c('2021-12-01','2023-02-15')))
dev.off()

# EOF